arXiv - MATH - Optimization and Control最新文献_第2页

On the control of LTI systems with rough control laws 用粗糙控制法控制 LTI 系统

arXiv - MATH - Optimization and Control Pub Date : 2024-09-18 DOI: arxiv-2409.11766

Lucas DavronCEREMADE

引用次数: 0

A Proximal Variable Smoothing for Nonsmooth Minimization Involving Weakly Convex Composite with MIMO Application 涉及弱凸复合的非光滑最小化的近端变量平滑法与多输入多输出应用

arXiv - MATH - Optimization and Control Pub Date : 2024-09-17 DOI: arxiv-2409.10934

Keita Kume, Isao Yamada

引用次数: 0

Nesterov's method of dichotomy via Order Oracle: The problem of optimizing a two-variable function on a square 通过 Order Oracle 的涅斯捷罗夫二分法：优化正方形上的双变量函数问题

arXiv - MATH - Optimization and Control Pub Date : 2024-09-17 DOI: arxiv-2409.11077

Boris Chervonenkis, Andrei Krasnov, Alexander Gasnikov, Aleksandr Lobanov

引用次数: 0

Relationship between stochastic maximum principle and dynamic programming principle under convex expectation 凸期望下随机最大原则与动态程序设计原则的关系

arXiv - MATH - Optimization and Control Pub Date : 2024-09-17 DOI: arxiv-2409.10987

Xiaojuan Li, Mingshang Hu

引用次数: 0

Birth control and turnpike property of Lotka-McKendrick models 洛特卡-麦肯德里克模型的生育控制和岔道特性

arXiv - MATH - Optimization and Control Pub Date : 2024-09-17 DOI: arxiv-2409.11247

Marius Bargo, Yacouba Simpore

引用次数: 0

Towards Optimal Spatio-Temporal Decomposition of Control-Related Sum-of-Squares Programs 实现与控制有关的平方和程序的最佳时空分解

arXiv - MATH - Optimization and Control Pub Date : 2024-09-17 DOI: arxiv-2409.11196

Vít Cibulka, Milan Korda, Tomáš Haniš

引用次数: 0

Balancing Optimality and Diversity: Human-Centered Decision Making through Generative Curation 平衡最优性与多样性：通过 "生成式策划 "进行以人为本的决策

arXiv - MATH - Optimization and Control Pub Date : 2024-09-17 DOI: arxiv-2409.11535

Michael Lingzhi Li, Shixiang Zhu

{"title":"Balancing Optimality and Diversity: Human-Centered Decision Making through Generative Curation","authors":"Michael Lingzhi Li, Shixiang Zhu","doi":"arxiv-2409.11535","DOIUrl":"https://doi.org/arxiv-2409.11535","url":null,"abstract":"The surge in data availability has inundated decision-makers with an\u0000overwhelming array of choices. While existing approaches focus on optimizing\u0000decisions based on quantifiable metrics, practical decision-making often\u0000requires balancing measurable quantitative criteria with unmeasurable\u0000qualitative factors embedded in the broader context. In such cases, algorithms\u0000can generate high-quality recommendations, but the final decision rests with\u0000the human, who must weigh both dimensions. We define the process of selecting\u0000the optimal set of algorithmic recommendations in this context as\u0000human-centered decision making. To address this challenge, we introduce a novel\u0000framework called generative curation, which optimizes the true desirability of\u0000decision options by integrating both quantitative and qualitative aspects. Our\u0000framework uses a Gaussian process to model unknown qualitative factors and\u0000derives a diversity metric that balances quantitative optimality with\u0000qualitative diversity. This trade-off enables the generation of a manageable\u0000subset of diverse, near-optimal actions that are robust to unknown qualitative\u0000preferences. To operationalize this framework, we propose two implementation\u0000approaches: a generative neural network architecture that produces a\u0000distribution $pi$ to efficiently sample a diverse set of near-optimal actions,\u0000and a sequential optimization method to iteratively generates solutions that\u0000can be easily incorporated into complex optimization formulations. We validate\u0000our approach with extensive datasets, demonstrating its effectiveness in\u0000enhancing decision-making processes across a range of complex environments,\u0000with significant implications for policy and management.","PeriodicalId":501286,"journal":{"name":"arXiv - MATH - Optimization and Control","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic Range Reduction via Branch-and-Bound 通过分支和边界缩小动态范围

arXiv - MATH - Optimization and Control Pub Date : 2024-09-17 DOI: arxiv-2409.10863

Thore Gerlach, Nico Piatkowski

{"title":"Dynamic Range Reduction via Branch-and-Bound","authors":"Thore Gerlach, Nico Piatkowski","doi":"arxiv-2409.10863","DOIUrl":"https://doi.org/arxiv-2409.10863","url":null,"abstract":"The demand for high-performance computing in machine learning and artificial\u0000intelligence has led to the development of specialized hardware accelerators\u0000like Tensor Processing Units (TPUs), Graphics Processing Units (GPUs), and\u0000Field-Programmable Gate Arrays (FPGAs). A key strategy to enhance these\u0000accelerators is the reduction of precision in arithmetic operations, which\u0000increases processing speed and lowers latency - crucial for real-time AI\u0000applications. Precision reduction minimizes memory bandwidth requirements and\u0000energy consumption, essential for large-scale and mobile deployments, and\u0000increases throughput by enabling more parallel operations per cycle, maximizing\u0000hardware resource utilization. This strategy is equally vital for solving\u0000NP-hard quadratic unconstrained binary optimization (QUBO) problems common in\u0000machine learning, which often require high precision for accurate\u0000representation. Special hardware solvers, such as quantum annealers, benefit\u0000significantly from precision reduction. This paper introduces a fully\u0000principled Branch-and-Bound algorithm for reducing precision needs in QUBO\u0000problems by utilizing dynamic range as a measure of complexity. Experiments\u0000validate our algorithm's effectiveness on an actual quantum annealer.","PeriodicalId":501286,"journal":{"name":"arXiv - MATH - Optimization and Control","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142254488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Contraction Analysis of Continuation Method for Suboptimal Model Predictive Control 次优模型预测控制连续法的收缩分析

arXiv - MATH - Optimization and Control Pub Date : 2024-09-17 DOI: arxiv-2409.10970

Ryotaro Shima, Yuji Ito, Tatsuya Miyano

引用次数: 0

Existence of eigensets on bilinear control systems 双线性控制系统上等位集的存在性

arXiv - MATH - Optimization and Control Pub Date : 2024-09-17 DOI: arxiv-2409.11194

Eduardo Celso Viscovini

引用次数: 0